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2017
Enhancing Engineering Education Using Mobile Augmented
Enhancing Engineering Education Using Mobile Augmented
Devices
Devices
Kushal AbhyankarWright State University - Main Campus
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ENHANCING ENGINEERING EDUCATION USING MOBILE AUGMENTED DEVICES
A Dissertation submitted in partial fulfillment of the
requirements for the degree of
Doctor of Philosophy
By
KUSHAL ABHYANKAR
M.S.EGR., Wright State University, 2011 M.S.EGR., Wright State University, 2008
B.E., University of Mumbai, 2005
_____________________________________
2017
WRIGHT STATE UNIVERSITY SCHOOL OF GRADUATE STUDIES
April 14, 2017
I HEREBY RECOMMEND THAT THE DISSERTATION PREPARED UNDER MY SUPERVISION BY Kushal Abhyankar ENTITLED Enhancing Engineering Education Using Mobile Augmented Devices. BE ACCEPTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF Doctor of Philosophy.
Subhashini Ganapathy, Ph.D. Dissertation Director
Frank Ciarallo, Ph.D. Director, Ph.D. in Engineering Program
Robert E. Fyffe, Ph.D. Vice President of Research and Dean of the Graduate School Committee on Final Examination
Subhashini Ganapathy, Ph.D. Mary Fendley, Ph.D. Xinhui Zhang, Ph.D. Nathan Klingbeil, Ph.D. Wayne C. Grant, Ph.D. Ling Rothrock, Ph.D.
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Abstract
Abhyankar, Kushal. Ph.D., Engineering Ph.D. Program, Department of Biomedical, Industrial and Human Factors Engineering, Wright State University, 2017. Enhancing Engineering Education Using Mobile Augmented Devices.
Employing effective and modern educational systems that support augmented learning methods such as mobile-based learning, may offer a promising solution to lowering dropout rates and to improving learning interests in engineering education. Mobile-based learning is capturing tremendous attention due to the affordances mobile devices can offer. This project outlines efforts to integrate mobile-based educational technology into the classroom. Leveraging the affordances, we designed a mobile augmented education tool for basic math and physics concepts that allows access to information and additional learning content within the context of classroom learning. Results from the study indicate that there is significant improvement in overall performance in mathematics and physics for all students. Based on the form-factor analysis, we found that the students highly preferred 7-inch tablet devices for the overall presentation of the content and portability. This research aims to present the framework and design guidelines for mobile-based augmented learning tools intended to enhance engineering education. The design guidelines presented in this research can universally be applied for any classroom assisting mobile augmented education tool. Structural equation model analysis of the questionnaire based data collected from the students also suggests that the designed model predicts the behavioral intention of the test participants accurately. It also proves the validity and reliability of the collected data. Model development process forms a systematic metric to understand the performance of mobile augmented education tools and develops a framework to assess the students’ overall attitude towards it. According to the horizon report, as education practices move from formal to informal and collaborative, mobile devices are playing a major role in the transition process. This research is an attempt to provide students with an ability to leverage their day to day devices to assist them with learning content for better knowledge acquisition.
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Table of Contents
1 INTRODUCTION ... 1
2 LITERATURE REVIEW... 9
2.1 MOBILE-BASED LEARNING ... 9
2.2 TAXONOMY OF EDUCATIONAL PRACTICES... 20
2.3 LEARNING STYLES ... 23
2.3.1 Formal Education Practices ... 24
2.3.2 Informal Education Practices ... 26
3 RESEARCH PHASE 1 - CONCEPT IDENTIFICATION ... 31
3.1 PHASE 1A RESEARCH ... 31
3.2 PHASE 1B RESEARCH ... 35
4 RESEARCH OBJECTIVES AND RESEARCH QUESTIONS ... 43
4.1 RESEARCH OBJECTIVES ... 43
4.2 RESEARCH QUESTIONS ... 44
5 MOBILE-BASED AUGMENTED LEARNING SYSTEM DESIGN ... 45
5.1 SYSTEM DESIGN ... 48
5.1.1 Accessing any other slide from any slide in the lecture stack ... 48
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5.1.3 Additional Learning Notes Provided by the Instructors ... 54
5.1.4 Interactive Graphs ... 56 5.1.5 Additional Videos ... 59 5.2 HEURISTIC EVALUATION ... 62 6 DESIGN OF EXPERIMENT ... 67 6.1 METHODS ... 67 6.1.1 Independent Variables... 67
6.2 TEST PROCEDURE DESCRIPTION ... 70
6.3 TESTING PROCEDURE ... 73
7 RESULTS ... 77
7.2 EFFECT OF MOBILE-BASED AUGMENTED LEARNING SYSTEM ON STUDENT GROUPS ... 81
7.3 BETWEEN GROUPS ANALYSIS – UNDERSTAND EFFECT OF TECHNOLOGY INTEGRATION AND ENGINEERING CONCEPTS ACROSS DIFFERENT STUDENT GROUPS ... 89
7.4 3-WAY ANOVA ... 97
7.4.1 Plots... 98
8 USER EXPERIENCE EVALUATION ... 102
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9.1 BACKGROUND ... 108
9.2 NEW MODEL STRUCTURE – USER-CENTERED TECHNOLOGY ACCEPTANCE MODEL ... 131
9.3 TEST OF VALIDITY AND RELIABILITY ... 135
9.4 CONSTRUCTS AND ANALYSIS PER CONSTRUCT ... 139
9.4.1 Behavioral Intention (BI) ... 139
9.4.2 Performance Expectancy (PE) ... 140
9.4.3 Effort Expectance (EE) ... 142
9.4.4 Social Influence (SI) ... 144
9.4.5 Facilitating Conditions (FC) ... 146
9.4.6 Self-Perception (SP) ... 147
9.4.7 User Experience (UX) ... 149
9.5 MODEL RESULTS DISCUSSION ... 151
10 DISCUSSIONS ... 153
10.1 PHASE 1: CONCEPT IDENTIFICATION ... 153
10.2 RESULTS DISCUSSION - STATISTICAL EXPERIMENTAL DESIGN AND MODEL DEVELOPMENT ... 153
10.3 RESULTS OF STRUCTURAL EQUATION MODELING ... 155
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10.4.1 Mobile-Based Augmented Learning System Design Guidelines ... 156 11 RESEARCH IMPLICATIONS ... 160 12 REFERENCES ... 164
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List of Figures
1.1 Attrition Rates in STEM courses over years 2003-2009 ……….….. 2
1.2 First Year Attrition Rates in STEM – Undergraduate Degree over years 2003-2009 ...…. 2
1.3 First Year Attrition Rates in STEM – Associates Degree over years 2003-2009 .…. 3
1.4 Attrition Rates in STEM as per High School GPA – Undergraduate Degree over years 2003-2009 ………..……...….. 3
1.5 Attrition Rates in STEM as per High School GPA – Associates Degree over years 2003-2009 ………..…… 4
1.6 Research Framework ……….…… 7 3.1 Phase 1a: Primary User-Centered research to collect the difficult subjects in the science education domain ……… 33
3.2 Phase 1a: Primary User-Centered research to collect the difficult subjects in the technology education domain ……….. 33
3.3 Phase 1a: Primary User-Centered research to collect the difficult subjects in the engineering education domain ………...……….. 34
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3.4 Phase 1a: Primary User-Centered research to collect the difficult subjects in the mathematics education domain ……… 34 3.5 Phase 1b: Deep Dive-user-centered research to collect the difficulties in the Science education domain ……….……… 37
3.6 Phase 1b: Deep Dive-user-centered research to collect the difficulties in the Technology education domain ………. 37
3.7 Phase 1b: Deep Dive-user-centered research to collect the difficulties in the Engineering education domain ……… 38
3.8 Phase 1b: Deep Dive-user-centered research to collect the difficulties in the Mathematics education domain ………...…… 38
3.9 Phase 1b: Deep Dive-user-centered research to understand the desired improvements in the Science education domain ……….. 39
3.10 Phase 1b: Deep Dive-user-centered research to understand the desired improvements in the Technology education domain ………...……….… 39
3.11 Phase 1b: Deep Dive-user-centered research to understand the desired improvements in the Engineering education domain ……….…….. 40 3.12 Figure 3.12: Phase 1b: Deep Dive-user-centered research to understand the desired improvements in the Mathematics education domain ……….……… 40
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5.1 Primary Content Screen ……….…….. 49
5.2 Screen showing the open tray with links to all the slides ………...……. 49
5.3 Typed notes icon ………...………... 50
5.4 Additional options showing notepad ..………. 51
5.5 Typing notes on the slide ………...….. 51
5.6 Marked notes icon ……… 52
5.7 Taking notes on the slides after clicking ‘Mark’………...…...… 53
5.8 Saved notes along with the original slide .………..……. 53
5.9 Additional content in the form of notes icon ………...…….……54
5.10 Additional options showing additional notes ………...……….. 55
5.11 Additional notes shown with the primary content slide minimized in the left top corner ………....……… 56
5.12 Interactive Graphs Icon ………..…….... 57
5.13 Additional learning materials showing the interactive graphs.………...…… 58
5.14 Interactive graph with pinch and zoom ability .………...………..… 58
5.15 Dropdown with prepopulated list of graphs and textbox on right to write the equation to plot the graph …………...………...…………..… 59
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5.16 Accessing Additional Videos Icon ………...…………..… 60
5.17 Additional learning materials showing the additional videos ……..…………..… 61
5.18 Additional learning videos with the primary content slide minimized in the left top corner. Video controls are seen in the image, and these controls appear on the click over the video ………...………..……..… 62
6.1 Study Picture 1………...………..…. 73
6.2 Study Picture 2………...………..…. 73 7.1 Plot of standard deviation Vs. means for three way interactions for all 3 factors ………..……. 98 7.2 Plot of variance Vs. means for three way interactions for all 3 factors ……..…..… 99 7.3 Plot of observed scores Vs residuals for three way interactions for all 3 factors ………..………….…….... 99 7.4 Plot of means for group 1 students for both technologies application stimuli for mathematics and physics concepts ………..………100 7.5 Plot of means for group 2 students for both technology application stimuli for mathematics and physics concepts ………... 100 7.6 Plot of means for group 3 students for both technology application stimuli for mathematics and physics concepts ………... 101
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8.1 Responses about additional learning assistants collected from test participants ….. 103
8.2 Responses collected from the test participants about their preferred mobile device size ……….…….... 104 8.3 Testing Procedure …...………106 8.4 Mobile-Augmented Learning Assistant Showing Taking Notes by Typing Feature ………. 106 8.5 Mobile-Augmented Learning Assistant Showing Graphing Tool Feature ………. 107 8.6 Test Participants Learning with the help of Mobile- Augmented Learning Assistants ………. 107 9.1 The User-centered Technology Acceptance Model Structure ……… 134 9.2 Behavioral Intention (BI) Model with the underlying constructs of Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating
Conditions(FC), Self-Perception (SP), Mobility (MOB) and User Experience (UX)
……….……… 139 9.3 Behavioral Intention (BI) as a standalone model. This structure of the model is achieved by collecting the responses to the questions designed specifically for this
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9.4 Performance Expectancy (PE) Model with the underlying constructs of Perceived Usefulness (PU), Extrinsic Motivation (EM), Job Fit (JF), Relative Advantage (RA), and Outcome Expectance (OE) ……….……... 141
9.5 Performance Expectancy as a standalone construct. This structure of the model is achieved by collecting the responses to the questions designed specifically for this construct ………. 142
9.6 Effort Expectancy (EE) as a Standalone Construct. This structure of the model is achieved by collecting the responses to the questions designed specifically for this
construct .……….………...… 143
9.7 Social Influence (SI) Model with the underlying constructs of Subjective Norm (SN), Social Factors (SF), and Image (IM) ……….………...…. 145 9.8 Social Influence as a Standalone Construct. This structure of the model is achieved by collecting the responses to the questions designed specifically for this construct ………. 146 9.9 Facilitating Conditions (FC) Model with the underlying constructs of Perceived Behavioral Control (PBC), and Compatibility (C) ……….... 147
9.10 Self-Perception (SP) Model with the underlying constructs of Attitude toward technology (ATT), Self-efficacy (SA), Perceived enjoyment (Pen), and Self-management of learning (SML) ….………. 149
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9.11 User Experience (UX) Model with the underlying constructs of Usability (USA), Desirability (DES), and Accessibility (ACC)……….……… 150
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List of Tables
3.1 Salient outcomes of the phase 1a research ………... 35 3.2 Salient outcomes of the phase 1b research ………...… 41 5.1 Landscape Analysis – Learning Applications ………..… 46
5.2 Landscape Analysis – Learning Applications with mobile augmented learning system …...………... 66
6.1 Treatment assignment for the groups ………...……… 72
7.1 Comparison of means of test scores of physics and mathematics for all participant groups learning with and without technology ……….. 79 7.2 Comparison of means of test scores of physics and mathematics for the participant groups learning with and without technology ……….. 80 7.3 Comparison of means of test scores of physics and mathematics for the participant from group 1 learning with and without technology ………... 84 7.4 Comparison of means of test scores of physics and mathematics for the participant from group 2 learning with and without technology ………...…… 86
7.5 Comparison of means of test scores of physics and mathematics for the participant from group 3 learning with and without technology ………..………. 87 7.6 Comparison of means of test scores between different subject groups for learning mathematics and physics with and without technology ………...… 90
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7.7 Comparison of means of test scores between different subject groups for learning mathematics with technology ………..…… 91 7.8 Comparison of means of test scores between different subject groups for learning physics with technology ………... 92 7.9 Comparison of means of test scores between different subject groups for learning mathematics without technology ………...…..… 94 7.10 Comparison of means of test scores between different subject groups for learning physics without technology ………...………...….……..… 95 9.1 Models and the individual core constructs under review ……….. 112 9.2 Individual Constructs of the models, definitions and their design background …. 117 9.3 User-centered Technology Acceptance Model and respective constructs ... 131 9.4 Fit indices values and conclusions of all constructs …………...………... 152 10.1 Design Guidelines for the Mobile-Based Augmented Learning Assistant …….. 157
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Acknowledgement
I dedicate my work to my mother, Mrs. Rekha Abhyankar, my father, Mr. Dinesh Abhyankar, my wife Mrs. Supriya Abhyankar, my daughter Shanaaya Abhyankar and to the memories of my sister Shalaka Abhyankar, my grandmother, Sudha Taskar, and my uncle and mentor, Jayant Chitre. My sincere thanks to my in-laws Dr. Sunil Deshpande, Mrs. Meenal Deshpande, Ms. Sujaya Deshpande and my whole family; Mr. and Mrs. Bendre, their daughters, Diya and Nitya. I also would like to extend my thanks to my friends Deeptaunshu, Tushar, Rohit, Veena, Aarti, Ayush, Ravi, Ramya, Sandeep, Jaswandi, Rahul Boduluri, Kelkars, Umranis and Kulkarnis. I also want to thank three special persons in my life, Dr. S. Prabhala, Dr. S. Narayanan and William Zembrodt. I also want to express my gratitude toward Nitin and Parul Soni, my aunts and uncles, Mrs. Surekha Deshpande, Mr.Deepak Deshpande, Mr. Satish Taskar, Mrs. Mangal Taskar, and Dr. Kiran Chitre. My parents always taught me to be honest and focused, my wife kept me on the path, believed in me, and all others helped me immensely to stay on.
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1 INTRODUCTION
There is an increasing demand for qualified engineers in the ever-advancing engineering industry. Providing the right skill-set and training to engineers to fulfill the needs of the industry has always been a primary goal of the engineering education system. While college enrollment numbers continue to rise in the U.S., the number of students graduating from engineering programs is on the decline [28]. Since 1993, The Science and Engineering Indicators, a biennial report by National Science Foundation, has indicated a constant increase in attrition in STEM enrollment and a decrease in the number of successful graduates for various engineering majors [104]. In 2013, the National Center for Education Statistics reported alarming attrition rates in engineering degree programs as well. Figure 1.1 through 1.5 represent attrition rates in overall STEM programs and in engineering degree programs according to the statistical analysis report provided by the US department of education [28].
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Figure 1.1: Attrition Rates in STEM courses over years 2003-2009 [28]
Figure 1.2: First Year Attrition Rates in STEM – Undergraduate Degree over years 2003-2009 [28] 20% 40% 21% 22% 41% 62% 0% 10% 20% 30% 40% 50% 60% 70%
Undergraduate Degree Associates Degree
A tt ri tion R ate in Pe rc e n tage
Attrition Rates in STEM
Dropped out without any degree Changed from STEM to non STEM Total 24% 15% 11% 8% 4% 2% 0% 5% 10% 15% 20% 25% 30%
First year STEM students who left
school
First year STEM students who switched major
First year STEM students who persisted in STEM A tt ri tion R ate in Pe rc e n tage
Undergraduate Degree
Percentage of Students with withdrawn/failed STEM Courses Percentage of withdrawn/failed STEM Courses out of all STEM Courses Attempted3
Figure 1.3: First Year Attrition Rates in STEM – Associates Degree over years 2003-2009 [28]
Figure 1.4: Attrition Rates in STEM as per High School GPA – Undergraduate Degree over years 2003-2009 [28] 32% 18% 11% 24% 16% 10% 3% 14% 0% 5% 10% 15% 20% 25% 30% 35%
First year STEM students who left
school
First year STEM students who switched major
First year STEM students who persisted in STEM Total A tt ri tion R ate in Pe rc e n tage
Associates Degree
Percentageof Students with withdrawn/f ailed STEM Courses Percentage of withdrawn/f ailed STEM Courses out of all STEM Courses Attempted 45.80% 24.60% 22.10% 14.10% 25.30% 32.90% 32.50% 25.50% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%
Less then 2.5 2.5-2.99 3-3.49 3.5 or Higher
A tt ri tion R ate in Pe rc e n tage
Attrition rate in Undergraduate Degree Program in
STEM according to High school GPA
Dropped out of school without any degree
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Figure 1.5: Attrition Rates in STEM as per High School GPA – Associates Degree over years 2003-2009 [28]
According to Geisinger & Raman, Becker, Grau-Valldosera & Minguillón, and Wallace & Mutooni, there is a direct correlation between the traditional learning mechanism and the abstract nature of engineering concepts associated with engineering students who dropout [16][59][156]. Education styles within engineering schools are mostly formal and traditional. The definition of the modern all rounded engineer is given by the Accreditation Board for Engineering and Technology (ABET). Among this criteria for successful engineering programs (Criteria for Accrediting Engineering Programs, 2012 – 2017), students need to demonstrate an ability to apply knowledge of mathematics, science, and engineering, as well as a deep understanding of the impact of engineering solutions in a global, economic, environmental, and societal context. Students should be able to apply these concepts in a multidisciplinary team [1][2][3][4]. Georke, and Becker
41.80% 37.50% 36.20% 21.80% 36.30% 30.40% 31.30% 30.80% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45%
Less then 2.5 2.5-2.99 3-3.49 3.5 or Higher
A tt ri tion R ate in Pe rc e n tage
Attrition rate in Undergraduate Degree Program in STEM
according to High school GPA
Dropped out of school without any degree Switched major
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have proved that purely formal, classroom-based learning falls short when educating large groups of engineering students and results in an increased number of drop outs [16][57]. In order to achieve a comprehensive understanding of engineering concepts, students commonly have to look for alternative ways to educate themselves in addition to their classroom-based learning. These non-traditional practices allow students to think outside the box of conventional learning. This helps to improve the overall educational experience by catering to the learning style of the individual.
Most engineering schools still employ teaching practices that involve classic classroom instruction-teacher interactions. These kinds of interactions do not allow students to learn and experience real world problems where implementation of classroom learnt theoretical concepts is necessary and therefore, the educational experience becomes less interesting and the nature of the concepts remain abstract [40][41][45] [48][51]. Hence, there is a need to understand and provide teaching practices that allow students to understand abstract concepts in engineering. As part of the development of such education systems, much of the focus is drawn towards the introduction of informal learning practices in the engineering curriculum [49][51]. Informal learning styles allow for problem-based learning, active communication with peers and instructors, technology assistance, and collaboration. Due to the deep roots of formal teaching practices within traditional engineering schools, a total paradigm shift from formal to informal practices may not be possible. Technology integration in educational practices could be thought of as a viable solution to bridging the gap between the two teaching practices. While there are examples of the use of technology that have helped resolve some of the difficulties in educational settings, these are silo solutions [34][96][97][124][125][126][127][139][140]. There is a
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strong need to understand how to integrate technology into educational practices in a way that makes learning more efficient for students. Therefore, understanding the affordances of technology integration and developing guidelines to assist in the development of supportive learning avenues and alternatives is necessary.
The focus of this research was to understand the effectiveness of technology integration for knowledge acquisition and for developing design guidelines to explore the affordances of mobile technology as an augmented learning tool for STEM education. Specific research objectives include:
1. Understanding the challenges present in knowledge acquisition of engineering concepts.
2. Developing a mobile-based augmented learning system to help students learn the basic engineering concepts in mathematics and physics supporting in-class learning. Also, assessing the performance and effectiveness of mobile technology intervention in the process of learning basic engineering concepts.
3. Developing a taxonomy of design guidelines to facilitate the development of supportive content for engineering students on mobile devices.
4. Defining a framework for a User-centered Technology Acceptance Model that allows validation of the design and can predict the behavioral intention of students who would utilize this mobile technology as a technology assistant.
In order to address the research objectives in a systematic manner a research framework was developed as shown in Figure 1.6. This framework details the various steps conducted towards a systematic methodology to identify and address the challenges students experience in learning engineering subjects.
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Figure 1.6: Research Framework
As shown in Figure 1.6, the research framework for this study was divided in four phases – Phase 1) Concept Identification; Phase 2) Mobile-Based Augmented Learning Assistant Development; Phase 3) Testing and Evaluation; Phase 4) Model Development and Constructs Validation. Phase 1 included detailed user-centered research to identify some of the most difficult engineering subjects, a detailed list of difficulties students face, as well as desired improvements in engineering education. In Phase 2, we developed the mobile-based augmented learning system. With the mobile-mobile-based augmented learning system, we designed mathematics and science (physics) classroom material with additional augmented learning assisting material in the instantiation stage. In Phase 3, testing was conducted on mobile augmented learning system participants with formal tests and questionnaire-based attitudinal measurements that allowed us to confirm the effectiveness of the technology integration as well as validity and reliability of the collected data. Post-test qualitative interviews helped us understand usability and desirability of individual leaning assistants.
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These interviews also helped in formation of overall design guidelines for the mobile augmented learning assistant designs. In Phase 4, we designed the ‘User-centered Technology Acceptance Model’. This design was used for modeling and testing the validity of the data collected to address the basic attitudinal measurements of the student participants toward their behavioral intention.
This overall framework is a result of individual research and development activities. This framework is scalable to any systematic mobile augmented learning assistant development and the testing for all-round effectiveness.
This dissertation is divided into 11 chapters. Chapter 2 presents the research background in review of the theory relevant to creating the framework. Chapter 3 (Concept identification) discusses the exploratory phase of the research. In Chapter 4, the research objective and hypotheses are presented. Chapter 5 discusses the mobile system design. Chapters 6 and 7 discuss the experiments conducted to examine and validate the framework. Chapter 8 presents the qualitative data collected from the participants to understand the preferred mobile device sizes and interesting learning assistants. Chapter 9 discusses the model development based on the quantifiable data collected. The results discussion is presented in Chapter 10. Lastly, the theoretical contributions and applications of this work are laid out in Chapter 11.
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2 LITERATURE REVIEW
2.1 MOBILE-BASED LEARNING
The pervasiveness of mobile devices is changing the way people interact with content and their surroundings. As the processing power of smartphones, smart watches, and tablets continue to increase dramatically, mobile learning or m-learning, enables learners to access materials anywhere, across multiple devices. Convenience is driving demand for this strategy, with the potential for new mobile-enhanced delivery models that can increase access to education. Instructors are harnessing the capabilities of mobile devices to foster deeper learning experiences by creating new opportunities for students to connect with course content. Mobile apps, for example, allow two-way communication in real time, helping educators efficiently respond to student needs. This development is impacting both the delivery and creation of educational content. Surveys of the field have revealed that instructors still need technical and pedagogical support from their institutions in integrating mobile devices into their curricula [31][72]. The Horizon Report, 2017 [72] has highlighted mobile technology as a prime form of technology that will be consumed in the educational field in next couple of years. Technology developers and visionary educators believe that mobile technology is going to be an inalienable part of human life for information exchange.
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Through the same vision, education researchers have begun exploring the potential of mobile technology for improving teaching and learning practices in education. Several researches have documented the implementation of mobile-based learning ecosystems [33][34][94][95][105][123][124][125][138][139][143].
Mobile learning devices can act as excellent catalysts in a rich active learning environment. Active learners are expected to act in the physical world (classrooms, projects, real world problem solving), access resources (text, sound or videos on the internet), and interact with others. Mobile devices can act as excellent mediators between the individual learner and his or her social and physical environment. As e-learning has started extending its branches, m-learning is coming up as a specialized branch [96]. Matthee and Liebenberg [97][98] have implemented mobile devices as mediating devices for basic mathematics courses for teenagers. The research scenario is based in South Africa. While PC-based education is limited there is a massive population of people using mobile devices for communication and education. The application is termed as ‘Mobi’; a mathematics support tool that teaches students about the basic concepts of mathematics in an active learning space. MyArtSpace™ is another mobile-based learning system for museums that is developed keeping the cohort of school students in mind. MyArtSpace™ software shows multimedia presentations of museum exhibits, takes photos, records voices, takes notes, and tells the user who else has viewed the exhibit. As backup, the content is stored to a server which maintains a personal record of student visits [33]. The research conducted by Stewart highlights the need for educators and education researchers to work closely together for the common good. This research combines socio-cultural perspectives with the context of learning materials and different pedagogical methods. The framework is designed for student-led active learning
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practices. This research and the framework have resulted in a significant increase in the level of student engagement [33][122][123][124][125][138][144][145][146]..
Bressler and Kahr-Hoijland [26] documented two interesting parallel projects in a mobile-based general science teaching area. The First project described is called Science Now, Science Everywhere (SNSE). SNSE is dedicated to learning about museum artifacts and animals. SNSE allows students to learn about these artifacts through voice narratives and SMS. Using SMS, visitors can update the information on exhibit displays. The second project, called ego-trap, allows student participants to learn about the science exhibits through a series of question-answer games. Although the area of application of these systems is different than engineering or core science, the application of mobile devices was noteworthy. The primary reason why it attracted attention was that the experimenters were able to use the mobile devices to effectively increase student’s interest in learning.[27]. Arevalo, et. al [15] and Reynolds [117] designed a training program for dentists who were mobile users and provided learning content on these devices. The extension of this system is a model-based virtual environment supported system for dentists’ training. Georke and Oliver [57] attempted to define the uses of mobile devices for university students. The focus of their research was to understand how mobile devices can be used as personal assistants. Their findings on PDA based research highlighted the use of mobile devices as an accessory or a secondary learning device. Students visualized these mobile devices as an addendum but not as primary learning devices. This research was carried out in 2005, before the birth of the mobile iOS and Android systems. The use of mobile devices in human life has grown from an accessory to an inseparable pervasive device. Mobile phones and tablets are just a few of the major devices that support reading practices and serve as a primary means of communication. There is limited evidence available to assess the role of the latest mobile devices as education assistants in
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engineering institutions. Therefore, through this research project, we attempt to; a) assess the influence of a mobile learning tool for knowledge acquisition using user-centered design methods; b) measure the inclination towards acceptance of the mobile technology for in class and outside-the-classroom learning settings; c) identify the level of knowledge acquisition using mobile-assisted teaching methods.
Hartnell-Young [63][64], Hartnell-Young and Vetere [66], and Hartnell-Young and Heym [65], highlight the necessity of educators and researchers to work hand-in-hand in order to implement mobile-based learning practices. Kukulska-Hulme [89][90] has demonstrated the effectiveness of the use of smartphones in informal and self-learning settings. These tools are essential in the development of hands on skills, professional development; solving real world problems with ease and so on. Mobile devices are useful for in-travel multimedia support for learning, reading, and editing when we are on the move. Studies conducted by Kukulska-Hulme [89][90][91][122][160] through multiple collaborations have integrated the mobile-based learning practices for several learning trainings as well as teaching aids. A primary focus of this research is around the deep understanding of a) content delivery, b) knowledge transfer, c) content presentation, d) social interaction with the help of mobile devices and e) collaborative learning opportunities through the use of mobile devices. Another important part of this research was to identify the avenues needed to integrate mobile technology with flexible yet effective teaching practices. In order to arrive at conclusive standards, experiments were conducted with the help of PDAs and palm computers. The standards and requirements set by this research, for mobile-based learning, are universal. Lefoe [93] described the importance of implementing scenario-based learning in mobile-based informal learning pedagogies. Mann [96] highlighted the need to utilize mobile devices within learning contexts as mediators .Research activity documented by Rentoul
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et al [116], Klopfer et.al [84], and Zembal-Saul et al [164] informs us that the practice of mobile-based learning is available as a growing group of documented case studies of innovative practices of technology supported active learning [85][116][164] . In order to understand student engagement and acceptance level of mobile-based active learning practices, Sharples and Taylor [124][125] conducted detailed ethnographic studies over students using mobile technology as a mediating device for learning. This ethnographic research identified the important role of mobile technology as a learning aid. Types of learning aids included: video streams, SMS services, and reading assistants.
In another example, Thompson and Stewart [136] presented the mobile-based learning assistant system called Jigsaw which specializes in science education for primary and secondary school students in the UK. This particular technology utilizes tablets, PCs, cameras, USB drives, and Wi-Fi connectivity. Students used these technologies while collecting information about the local environment to create products such as databases for plant species. The successful application of this strategy enabled teachers to incorporate mobile learning into their classrooms. Mobile services offer a multitude of learning opportunities and aids for improved reading practices. Podcasts are one of the services available on mobile devices [136] . Research carried out by Clark, Sutton-Brady, Scott, and Taylor [29] showed a significant affinity for the mobile podcast based learning content. The results also showed a significant improvement in the students’ long term knowledge retention levels through this mode of learning. Service enriched mobile devices are an excellent option for on-the-spot learning which is an inseparable form of active learning that is often incorporated in jobs and training situations. With the help of Information and Communication Technologies (ICT), the experience of on-the-spot training can be enhanced. Ferry [52] reports the use of cell phone devices rich with multiple multimedia, communication, and
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document services in training teachers for primary schools. Herrington and Herrington [69] presented with a similar system, approached from a design perspective, in the area of teachers’ education. They also strived to extend the utilization of mobile devices in higher education within multiple research applications. Herrington et Al. [70] studied the implementation of mobile learning devices such as palm smart phones and iPods for learning support with mp3 recordings in the space of educators’ training. Kervin and Mantei [80] also presented research on the use of iPods in the area of educator training.
Language learning is another field that is enriched by the assistance of mobile devices. Pearson [107] developed a family oriented English language learning tool, using mobile phones, for eastern European immigrants. These mobile-based learning practices showed significant improvements in language skills of test subjects. Improvements were seen in areas such as: speaking, writing, reading, and also understanding the spoken language. Hwang, Chen and Chen [74] developed a mobile-based scaffolding tool for the development of English writing skills in those who seek to learn English as a foreign language. Deng and Shao [38] presented a mobile-based English vocabulary learning tool which led to better confidence as well as improved self-direction towards learning. Students demonstrated higher acceptance levels of mobile devices as scaffolding tools. These results reflect a high sustainability of mobile-based active learning practices in vocabulary learning. Petersen, Sell, and Watts [109] designed a mobile-based language learning aid called Cloudbank, which supports a repository for words and expressions that can be shared by several users. The experiment was carried out at an international school using a mobile application designed to support language learning. This was accomplished by teaching students figures of speech in the English language. Mobility and accessibility of the content and
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information, appropriation of the technology, collaboration among students, additional content availability, and ownership of the learning content were common outcomes from this experiment.
In the field of science education, research on mobile-based educational practices looks promising. Johnson, Davison and Moralejo [76] presented a mobile–based learning platform for nursing students who learned English as a foreign language. In international universities, many times, the support of manuals and textbooks are forbidden. Nursing education demands on-the-spot and just-in-time learning. Mobile devices are best suited for this purpose. The mobile supported just-in-time learning setting has proven to be extremely useful in student learning as well as increasing self-efficacy towards learning practices. Ernst and Harrison [39] developed SBLi™ a mobile device interface that delivered knowledge to the biomedical students learning in just-in-time settings. SBLi™ delivers short 90 second informational videos on several information snippets in the physiology practical classes. Contextual learning is another widely researched field. In the laboratory setting, SBLi™ offers high impact active learning opportunities vs. formal teaching environments at convenient times. This system was designed to deliver context aware content for the learners in several different physical settings. Tan, Zhang, Kinshuk and McGreal [134] designed an innovative 5R adaptation framework to deliver context aware learning content to students. This framework has been tested for its effectiveness for field trips’ in outdoor education settings. The subsequent content is generated considering: location, time, learner, and type of mobile device in use. Morimoto et Al. [101] showcased a dynamic content construction model for the generation of learning content. Generated content was specific to the mobile device and based on the form factor size, learner ability, and contextual adaptability. Though the authors have not produced conclusive experimental results, the development of the auto-learning content generating model is a positive initiative from the Japanese society of science promotion. Cochrane,
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Narayan and Oldfield [30] integrated iPads in several engineering streams over multiple longitudinal studies that examined the effectiveness of technology integration. The study was carried out in multiple streams of education over multiple time periods. For each education stream, the mobile-based learning platform presented a different set of affordances. The tested streams were architecture, music education, business education, and civil engineering education. The pedagogy was not modified but devices were provided in the classroom to provide support for student needs such as: additional content searches and illustrations. Ipads were not used in class for the primary content presentation. Besides calculators, all services were standard web-based existing applications such as google docs, microbloggers, and polling. In this line of research, multiple observational studies have been performed for landscape design and product design degrees. There has been only one study with respect to engineering, which does not support hosting primary learning content on the mobile device. Along with the research on iPad interventions in the classroom settings, Cochrane [30] also highlighted the necessity of a dedicated course assessment for technology assisted learning.
As mobile devices and underlying computing technology evolves, these advances can be utilized for the betterment of education structure and knowledge delivery. Many researchers agree that mobile-based learning facilitates improved interest and retention in students and has the potential to propel the overall manner in which we teach or learn. Personalization of the content, education beyond the classroom walls, and more emotional connection to the learning content can be provided by universal mobile technology. This content provision can be personalized with the help of several attributes. Personal profiling, individual responses, and cognitive load assessment are some of the attributes that can be utilized to present the profiled content to the learner. M-learning practices can be tailored to provide a rich personalized M-learning experience to serve
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students with various learning and research interests [137][138][139][140]. Ng [103] suggested personalized content on the mobile devices of learners. Even if a practically functioning system was never built, a conceptual architecture is presented to drive content personalization and hybrid learning. Mobile-based learning also aims to engage a massive user interest by improving learner engagement. Therefore, researchers have been trying to focus on the production of intriguing learning content in order to support the cause. The intentions of mobile-based learning go beyond the classroom. De Waard et Al. [37] tried to assess interest level in learners with the help of mobile technology. This research was conducted to support remote collaboration, synergy, active dialogue, and long-term retention. Mobile-based online courses (MobilMOOCS) are some of the most popular mobile-based learning platforms. De Waard et. Al predominantly used MobilMOOCS to study enhanced engagement using mobile devices in learners. With similar intentions of educating adults and including them in the stream of active learners, Slakovic and Savic [127] conducted a comprehensive survey of 347 adults living in Serbia in the age group of 60 to 75. The authors found that most of the adults were interested in computer science, art, and foreign language training. Results also revealed that that they desired long lasting education platforms. By understanding the usage patterns of the users and their smart devices, authors also concluded that adults in this age group could utilize mobile devices effectively and educate themselves efficiently with longer retention. Perez, von Isenburg, Yu, Tuttle and Adams [108] reported a significant increase in the utilization and satisfaction of online resources when medical trainees at Duke University were allowed to use the hospital distributed iPads. The frequency of visits to the most versatile medical databases, PubMed and DynaMed, increased significantly. This usage data was self-reported. Factors that hindered students from benefiting from the affordances of the mobile devices were the feasibility of carrying the device, internet connection, and
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accessibility of medical records content. Alamoud, Ganapathy, and McCarthy [8] provided evidence that the 7-inch tablet size was ideal for medical trainees and their daily lives on campus. This device size fits perfectly in the scrub pockets allowing easy content availability on the run. Therefore, the 7 inch mobile tablets can be viewed as ideal training devices for a large spectrum of learners.
An innovative system designed by Kalloo and Mohan [77] called MobileMath teaches mathematics to high school students in an innovative way. MobileMath uses alternative teaching practices with gamified content, and fun class activities. MobileMath has created a massive impact on students’ overall mathematics performance. González et al. [58] designed a mobile-based physics learning system, which has proved to be effective on students’ performance while learning physics. With this mobile-based learning system, students utilized mobile devices in laboratories as measurement devices, which opens up a massive opportunity to integrate mobile devices into laboratory settings. This can lead to the development of inexpensive yet efficient physics laboratories, which further research and provide valuable learning experiences for students.
The overall development of the learning content for any learner can follow either one or a combination of two carefully noted metaphors, which are game and cinematic experience [106]. The gaming metaphor engages the learners through the common elements associated with the gaming: such as competition, excitement, and instant gratification. The cinematic metaphor tries to convey the learning information through elements such as: narrations, reading content, and stories [155]. Staying parallel with a systematic and organized delivery of the content, it is imperative to provide the learner with the best learning and usage experience. This means that the user should not just receive the best usability by the device and the content but also the content should be desirable, credible, and pleasing [112][113]. This allows for a detailed user experience
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honeycomb-based inspection of the learning content [110]. Parsons, Ryu and Cranshaw [106] also stress the importance that the delivered content should not just be logically correct and strict to the metaphorical rules but it should also be conceivable and user-centered. They also presented with the design framework to understand the learner requirements, as well as design, develop, and test the mobile-based learning material to provide a better personalized learning experience.
Mobile-based learning has provided multiple examples of performance improvement. The success can be mapped on multiple dimensions. These dimensions are success in formal test results, improved retention, and increased involvement in the learning process. Cochrane [30] published some of the critical mobile-learning related factors such as course assessment, instructor involvement, and success of educational tools. Wang, Shen, Novak and Pan [157] reported a successful integration of m-learning practice for a massive scale English learning classroom. This success was reported as significantly increased involvement in the overall learning process.
From the literature review, it is evident that there is a lack of a systematic approach to understanding learning roadblocks and providing technology-based solutions for engineering students for learning STEM concepts. Addressing these shortcomings through technology assistance, specifically mobile-scaffolding, still lacks multiple important components such as - providing support for classroom content; additional instructor designed notes; efficient note making; and so on. Potential steps to provide a holistic solution for improved student engagement are conducting deep dive user research, developing appropriate learning content, testing for effectiveness, and understanding the attitudes of the students towards technology. Even though the efforts towards the development of an ecosystem for mobile-based education is rapidly advancing, these efforts are seen to be deviating from the basic principle of providing mobile-based educational support on primary learning content.
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For the mobile-based learning systems to be effective, it is important to understand the educational structure and the needs of students. Therefore, a detailed study of educational structure and its taxonomy becomes important to identify exact technology integration points.
2.2 TAXONOMY OF EDUCATIONAL PRACTICES
The objectives of the educational practices are defined by Bloom [20], and Bloom et. al [22]. These objectives are arranged in the form of taxonomy. The development of this taxonomy facilitates a common language of learning goals between educators, curriculum designers, and administrators. These objectives form a common basis for the determination of educational goals, evaluation, and reporting. The traditional Bloom’s educational taxonomy is defined in six different steps of learning. These steps are termed as (Knowledge, Comprehension, Application, Analysis, Synthesis and Evaluation). The definition of these levels is as follows:
Knowledge: In the knowledge level of Bloom's Taxonomy, questions are asked solely to test whether a student has gained specific information from the lesson.
Comprehension: The comprehension level of Bloom's Taxonomy has students go past simply recalling facts and instead has them understanding the information. With this level, they will be able to interpret the facts.
Application: In application questions, students have to actually apply, or use, the knowledge they have learned. They might be asked to solve a problem with the information they have gained in class being necessary to come up with a viable solution for a real-world problem.
Analysis: In the analysis level, students are expected to go beyond two previous levels, knowledge and application and actually come up with patterns that they can apply towards analyzing a real-world problem.
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Synthesis: Within synthesis level, students are required to use the given facts to create new theories and make predictions according to these formed theories. They are expected to apply knowledge (learnt or concluded) from multiple subjects and logically synthesize this information to come up to a conclusion.
Evaluation: This is the top level of Bloom's Taxonomy of educational objectives. Here students are expected to assess the information related to a much larger real world problem involving multiple disciplines, and requires knowledge from multiple subjects learnt. Students are also expected to come to a conclusion such as the value, bias for the information, or form hypothesis about it [21][22][111].
Levels 1-3 (Knowledge, Comprehension and Application) in the taxonomy are called as Low-level learning skills and levels 4-6 are called as high-level learning skills. Felder and Brent [40], Felder and Silverman [44], Felder, Woods, Stice, and Rugarcia [46], Felder, and Brent [49] have shown that the general classroom teaching and evaluation is able to assess the effectiveness only up to the first three levels of the Bloom’s taxonomy of educational objectives. The other three levels: namely, analysis, synthesis, and evaluation are real world application dependent, require the demonstration of experience from the subject and duration of time for which the knowledge is showcased. These levels require a longer time for assessment and also the expertise to understand the subject’s performance output over a larger timeline. In order to pass any judgment about any practice for classroom based learning, the best possible marker on the effectiveness of the testing procedure is the test for students’ knowledge acquisition and retention test. The traditional Bloom’s taxonomy and the revised Bloom’s taxonomy both follow a pyramidal structure starting from the base knowledge to the evaluation level [21][22][51][53].
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Krathwohl, Bloom, and Masia [88] found that most educational objectives can be placed into three major learning domains: cognitive, affective, and psychomotor. A major difference in the original and revised taxonomy with three leaning domains is that the original taxonomy has defined the learning abilities only for one (cognitive) domain. These domains are defined as follows:
Cognitive learning domain:
Bloom, Engelhart, Furst, Hill, and Krathwohl [22] defined the cognitive education domain as recalling or the recognition of knowledge and the development of intellectual abilities and skillsets. Cognitive domain represents the entire definition of Bloom’s original taxonomy of educational objectives which is defined in the previous section.
Affective learning domain:
According to Kearney [79], affective learning is defined as the increasing internalization of positive attitudes and empathy towards content, subject matter, and teaching methodologies. Krathwohl, Bloom and Masia [88] and Anderson et. Al [10] and Rovai, Wighting, Baker, and Grooms [119] have shown that the affective learning domain deals with interests, opinions, emotions, attitudes, and values related to the educational practices, assisting technology, and overall learning process.
Psychomotor learning domain:
The outcome of this learning process is ultimately showcased when the acquired knowledge is transcribed into the motor skills or speed, dexterity, grace towards the application of skills [51][119][126].
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In summary, the cognitive domain involves knowledge and the development of intellectual skills. Affective domain describes learning objectives that emphasize a feeling, tone, emotion, or a degree of acceptance or rejection. This domain includes the manner in which we deal with things emotionally such as feelings, values, appreciation, enthusiasms, motivations, and attitudes [148][149][150][151][152][153][154][163].
The affective education domain is essential in understanding how scaffolding intervention is acceptable in terms of performance enhancement and overall positive attitude of the students. Psychomotor domain describes the educational objectives related to the motor and physical coordination achieved by the learner through learning practices [10][21][22]. For classroom practices and traditional learning in confined time envelopes, it is very difficult to measure the objectives of the psychomotor domain. The action of integrating technology in the classroom environment has immediate measurable outcomes which can be measured in the cognitive and the affective domain of the educational objectives. The educational objectives are designed in order to define the methodologies of delivering the knowledge students in efficient ways. These objectives also provide critical benchmarking steps in order to form detailed assessment of the teaching methodologies [128][129].
2.3 LEARNING STYLES
Educational practices define the way in which the students interact in terms of exchanging knowledge. Educational practices not only determine the success of the knowledge transfer process but also determine the attitude and the values that become engrained in the students. The educational practices are generally distinguished between two categories: formal and informal. The description and differentiation between the two is as follows:
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2.3.1 Formal Education Practices
The assignment of traditional teaching practices into the educational domain is termed as deductive teaching practices [40][41][43][44][45][156]. These teaching practices start with the concept and end in the application of the concept into real world problems. This is called deductive learning. The deductive learning practice typically begins with the introduction of the concept into the formal classroom setting. The instructor may introduce this concept to the students with the help of printed literature or textbooks. The concept may be in the form of a theorem or a principle printed in a textbook in relation to the subject being taught. This concept is then unfolded to the students with the help of theoretical proofs and derivations. The students then learn the theorem proofs by heart in order to further drive the development of the concept. The instructor then introduces some of the theory based problems in the form of homework, assignments, labs and projects which are based on the theorems. The students make the individual efforts to solve these problems. After these theory-based problems, students are introduced to the word problems which is one of the key challenges for the engineering students. Generally, in deductive learning practices, the application of the actual concept comes when the student is exposed to real world problem or when the student actually starts working in the industry which demands application of these learnt concepts in practice. These deductive teaching practices are in the category of ‘Chalk and board’ traditional teaching. These teaching practices are instructor centered [40][42][44][45][47][48]. The data from the first round of the user-centered research carried out with the engineering students at Wright State University unanimously confirms this problem, especially in the field of Mathematics. The word problems are designed for the students to comfortably implement their knowledge in the industry. However, when students are faced with these challenges in industry, it has proven to be one of the toughest areas for [5][6]. This problem
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arises due to the fact that the students are not familiar with the problem-solving environments and there is not enough practice for the students to have hands on knowledge about the problem based knowledge acquisition. When students are not able to make solid connections between word problems and real world hurdles, the entire teaching process becomes a loop.
In formal learning practices, students’ performance is based on practice problems and the connection students can maintain between the theoretical and real world problems. There is very limited technology involvement in the classroom. Since the classes are instructor driven, there is very limited active communication between peers. Additional learning materials are provided by the instructor so students are instructor dependent in terms of gaining knowledge and additional practice. Deductive teaching may better promote short term retention of factual information [40][43][44][114][115][120]. The graduating students learned with the help of deductive teaching practices, and real world exposure. Due to the lack of development of underlying concepts, lack of training in necessary skills, and deficiency in technological exposure, engineering students often lose interest and opt to drop out of the engineering curriculum [16][17][59][60][61][156][162].
The following are the general properties of formal learning practices:
• Formal learning practices are teacher driven, teacher centered and teacher directed education practices. These education practices are traditional and are followed in most of the learning institutions by the educators with large classroom sizes. • They are closed ended, allow for very less active discussions involving peers and
the instructors.
• The formal learning practices are structured and follow the strict knowledge formation pattern.
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• The process of knowledge transfer is strictly curriculum based and the context of learning is only limited to classroom level. Due to the sequential structure of these learning practices, there are few chances to come back into the curriculum or catch up with the class if a lecture is missed by the student.
• The knowledge transfer measurements are empirical and there is a little chance for holistic assessment of the knowledge transfer along with its effectiveness to be applied to the real-world problems. Students work on the problems and the revisions of the knowledge solitarily with generally asynchronous communication with instructors [71][161].
2.3.2 Informal Education Practices
The idea of informal learning practices begins with the idea of providing a holistic learning experience for students. Informal education practices are designed to transform teacher -centered learning to student-centered learning. Informal learning practices are focused on the development of students and their conceptual knowledge base [25][44][46][135]. Since informal learning practices are primarily focused on the development of students’ ability to apply the gained knowledge, they are granted complete freedom to choose the schedule and resources they utilize during the learning period. Therefore, these learning practices allow for the integration of technology and its changing dynamics. Due to advancement in the internet and in handheld devices, reading practices and self-educating methods are constantly being reshaped. Informal teaching methods offer a solution to accommodate this reshaping of technology [92][137][140]. Informal learning practices follow the inductive way of transferring the knowledge. Inductive learning practices begin with the introduction of real-world problems. Students are allowed to struggle positively in order to build their knowledge base while solving problems. In order to build
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knowledge, students are allowed to use several different means. They can utilize different technologies, information sources, team collaborations, and group discussions. Multiple different models concerning instructional methodologies have been defined. The following is a highlight of different inductive teaching models, which are commonly employed:
Inquiry-based Instruction (Inquiry-based or challenge-based learning): Teaching begins with an introduction of the problem or a challenge. The instructor provides the content guided to answer that problem. Instructors work with the students as guides if the students find themselves struggling to find the answers. In this situation, the instructor provides the students with additional support with learning material.
Problem-based Learning: The focus is to address a problem as authentic, open ended, or
not well defined. The students work in coordinated teams and instructor support is minimal. Students are pushed to learn new concepts in problem based learning.
Project-based Learning (Abbreviated as PBL) and Hybrid (problem/project based learning): Students are assigned some kind of project or design to build. In project based instruction, students are free to apply their previous knowledge to relevant projects.
Case-based Learning: Students examine case studies that involve the concepts and methods that the instructor needs to teach. Students work out problems involved in the case and compare those solutions with real world solutions.
Discovery Learning: Students are exposed to real world scenarios directly with minimal or no instructor support. They are observed in the process.
Just-In-time teaching: This method uses the technology support and just before the class begins, the instructor allows the student to answer a few questions and submit the answers electronically. The instructor responds in the same way and reveals the answers[40][42][43][47].
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The following are the properties of the informal learning:
Informal learning practices follow unstructured and unsequenced methods of teaching and learning Informal learning practices are student centered and student focused. Knowledge transfer is generally not evaluated through a formal examination process but rather through the application of knowledge to real-world problems. Informal learning activities are non-assessed and unevaluated, but they are reflected through the students’ ability to solve the problem. Informal learning practices are generally non-curriculum based and are outside of school context. These learning practices are designed in order to take the learning experience beyond the walls of a regular classroom. Since these learning practices are student-centered, these methods set students free for thinking through and apply their knowledge and abilities to perform certain tasks. Due to their unstructured nature, there are several unintended outcomes. Since the informal learning practices are learner led and learner directed, they are open ended and allow for students to gather as much knowledge as they wish. Along with just the primary learning concept, students learn several other concepts. One of the major benefits of informal learning practices is that the student is not alone. He or she is always engaged in social interactions, collaborating on a team, or are working across different platforms. While informal learning practices are learner led, the supervisory control is always with the teacher who administers the entire learning process [62][71][161].
One of the branches of informal learning practices is active learning, which focuses on the introduction of learning practices, which generate students’ rapport with the learning content. The goals and properties of active learning are explained in the section below.